Cao et al. proposed in [1]} a novel encoder-decoder network, named Scale Aggregation Network (SANet). Inspired by the success of the Inception architecture [2]} in the image recognition domain, the authors decided to employ scale aggregation modules in the encoder to improve the representation ability and scale diversity of the features. The decoder is instead composed of a set of convolutions and transposed convolutions. The latter is exploited to generate high-resolution and high-quality density maps, of which the sizes are the same as input images. Inspired by [3]}, they employed a combination of Euclidean loss and local pattern consistency loss to exploit the local correlation in density maps. In particular, the local pattern consistency loss is computed by the SSIM [4]} index to measure the structural similarity between the estimated density map and the corresponding ground truth. Finally, they used Instance Normalization (IN) [5]} layers to alleviate the vanishing gradient problem and a simple but effective patch-based training and testing scheme to diminish the impact of statistical shifts between training and test images.
